Many disciplines can benefit from pattern recognition. Disciplines where the benefit is greatest share characteristics and needs. Some common characteristics include large volumes of data, anomalous zones of interest that are mixed together with a large number of similar non-anomalous zones, timeframes too short to allow rigorous manual examination, and anomalies that manifest themselves in many ways, no two of which are exactly the same. Analysis of the data is usually done by highly trained professionals working on tight time schedules. Examples of these disciplines include, but are not limited to, hydrocarbon exploration and medical testing.
Exploring for hydrocarbon reservoirs is a very competitive process. Decisions affecting large amounts of capital investment are made in a time-constrained environment based on massive amounts of technical data. The process begins with physical measurements that indicate the configuration and selected properties of subsurface strata in an area of interest. The technical data include seismic signals (acoustic waves) that are introduced into the subsurface and reflected back to measurement stations on or near the surface of the Earth. A variety of mathematical manipulations of the data are performed by computer to form displays that is used by an interpreter, who interprets the data in view of facts and theories about the subsurface. The interpretations may lead to decisions for bidding on leases or drilling of wells.
Processing of seismic data has progressed hand-in-hand with the increased availability and capabilities of computer hardware. Calculations performed per mile of seismic data collected have increased many-fold in the past few years. Display hardware for observation by a human interpreter has become much more versatile.
When an interpreter uses data from the seismic process, it is used with some knowledge of geology of the area being investigated. The rationale for the decisions made based on the geologic information and the seismic data is not generally documented in detail. Therefore, it is difficult to review the history of exploration decisions using conventional procedures. The relative importance attached to the many characteristics shown in the seismic data and known from the geology is a subjective value that does not become a part of the record of the exploration process.
It is recognized that seismic data can also be used to obtain detailed information regarding producing oil or gas reservoirs and to monitor changes in the reservoir caused by fluid movement. Description of neural network modeling for seismic pattern recognition or seismic facies analysis in an oil reservoir is described, for example, in “Seismic-Pattern Recognition Applied to an Ultra Deep-Water Oilfield,” Journal of Petroleum Technology August, 2001, page 41). Time-lapse seismic measurements for monitoring fluid movement in a reservoir are well known. The fluid displacement may be caused by natural influx of reservoir fluid, such as displacement of oil by water or gas, or may be caused by injection of water, steam or other fluids. Pressure depletion of a reservoir may also cause changes in seismic wave propagation that can be detected. From these data, decisions on where to drill wells, production rates of different wells and other operational decisions may be made. The neural network technique usually assumes that all significant combinations of rock type are known before analysis is started so that they can be used as a training set. This assumption is usually acceptable when analyzing fully developed fields but breaks down when only a few or no wells have been drilled. The implementation of the neural network technique usually requires using data of pieces of fixed thickness that is centered on the geology of interest. Selection of the location of the geology of interest is an input that is determined prior to the analysis. As the geology of interest is not always well known, the geology of interest should be a product of the analysis, not an input. Moreover, geology of interest rarely has a fixed thickness. The thickness varies significantly as the depositional process varies from place to place, sometimes by an amount that is sufficient to significantly degrade the result of the neural network analysis.
U.S. Pat. No. 6,236,942 B1 discloses a neural network-based system for delineating spatially dependent objects in the subsurface from seismic data. The application of neural networks to seismic data interpretation has been widely investigated.
U.S. Pat. No. 6,226,596 B1 discloses the use of a Voxel Coupling Matrix, which is developed using a finite number of neighboring voxels forming a textile. “Texture attributes” are developed. The attribute volumes are then used as inputs into an attribute-trace classification method to produce a seismic interpretation volume. The interpretation volume is displayed using distinct colors to represent various numbers of classes of reflection patterns present within the seismic volume. The textile technique has a significant trade off. While larger textiles (blocks of neighboring voxels) give better results, larger textiles smear out and blur the resulting image. Success in finding a textile size that gives results of adequate quality with sufficiently small blurring is often very difficult, especially when the rock layers are dipping rather than horizontally flat.
U.S. Pat. No. 6,151,555 discloses a workstation computer system and an associated method and program storage device. U.S. Pat. No. 6,131,071 discloses a method for processing seismic data to provide improved quantification and visualization of subtle seismic thin bed tuning effects and other lateral rock discontinuities. A reflection from a thin bed has a characteristic expression and the frequency domain that is indicative of the thickness of the bed. The method may be applied to any collection of spatially related seismic traces. Other methods of presentation of seismic data are disclosed in the patent and technical literature.
What is needed is a way to perform unsupervised pattern analysis that does not require a learning set, and that does not require a-priori knowledge of the location of the geology of interest. Unsupervised pattern analysis requires feature, pattern, and texture extraction from seismic data where the features, patterns, and textures are well chosen for optimal classification. Optimal means that they:                Have variable lengths so that they track the rocks, organs, tissues, or other items being analyzed;        Have the minimum number of measurements to maximize computation simplicity;        Have an adequate number of measurements to separate out the rock or tissue types as uniquely as the data allows;        Are intuitive to geoscientists physicians, or other specialists in that they measure the visual characteristics of the data that the geoscientists use when they visually analyze the data;        Determine the locations of the different rock or tissue types as a product of the analysis; and        Use patterns, which are variable length spatial distributions of features, and textures, which are spatial distributions of patterns, in addition to features to perform the analysis.        
There is further a need in the art to generate patterns visually, from features in a pattern abstraction database, and to generate the patterns from multiple features. From a production standpoint, there is a need in the geoscience art to visually analyze the interior of a hydrocarbon reservoir more effectively. There is also a need in the medical art to examine the interior organs more effectively. Direct hydrocarbon or tumor indicators, for example, should be visually identifiable. Seismic stratigraphy should be used in a way that includes all the seismic stratigraphic information available in the data.
According to “The Basics of MRI,” by Joseph P. Hornak, Ph.D. (which is available online at: http://www.cis.rit.edu/htbooks/mri/):                “Magnetic resonance imaging (‘MRI’) is an imaging technique used primarily in medical settings to produce high quality images of the inside of the human body. MRI is based on the principles of nuclear magnetic resonance (‘NMR’), a spectroscopic technique used by scientists to obtain microscopic chemical and physical information about molecules. The technique was called magnetic resonance imaging rather than nuclear magnetic resonance imaging (‘NMRI’) because of the negative connotations associated with the word nuclear in the late 1970's. MRI started out as a tomographic imaging technique, that is it produced an image of the NMR signal in a thin slice through the human body. MRI has advanced beyond a tomographic imaging technique to a volume imaging technique.”        
While MRI produces finely detailed images of structures and features within the human body, it does not interpret those images. A trained physician or specialist performs the interpretation. Unfortunately, reliance upon a relatively few qualified individuals increases the cost of the interpretation process and limits the number of interpretations that can be made within a given period. This makes current MRI techniques impractical for standard screening procedures. As in seismic analysis, there is a need in the art for a knowledge capture technique where the data for MRI that the specialist looks at is captured by a pattern recognition process. Ideally, the pattern recognition process would be repeated for large amounts of data in a screening process, with the results displayed in an intuitive manner so that the specialist can quickly perform quality control on the results, and correct noise induced errors, if any.
There is further a need in the art for a way to auto-track textures, patterns, and features in order to isolate and measure rock bodies or body tissues of interest. Preferably, an object should be auto-tracked so that its location is determined both by the properties of its interface with surrounding objects, and by the difference between the features, patterns, and textures in the objects interior when compared to those outside the object. This tracks the object directly rather than tracking the object solely based on the varying properties of the interface which, by itself, is unlikely to be as descriptive of the object. Interface tracking tracks the object indirectly, as would be done with boundary representations. An example of automatically detecting objects based on their interior and interface characteristics would be in colorectal cancer screening where the target anomaly (a colorectal polyp) has both distinctive interface and interior characteristics.
Moreover, a data analysis specialist should not be required to rely on analysis of non-visual measures of object characteristics. The information describing the visual characteristics of seismic data or tissue information, should be stored in a way that allows the specialist to interact with the information to infer and extract geological or medical information and to make a record of the exploration process. Finally, a way should be provided to analyze geologic or medical information with varying levels of abstraction.
These needs are shared across many disciplines yet the specific nature of the data and the characteristics of the anomalies vary across disciplines and sometimes within a single problem. Thus, there is a need in the art for a common method of analysis that can be applied to a wide variety of data types and problems, yet can be adapted to the specific data and problem being solved in situations where required.